analytcis model

Code
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import adjusted_rand_score
eda = pd.read_csv("data/eda_data.csv")
Code

eda = pd.read_csv("data/eda_data.csv")

features = eda[['SALARY', 'MAX_YEARS_EXPERIENCE', 'MIN_YEARS_EXPERIENCE']].copy()

for col in ['MAX_YEARS_EXPERIENCE', 'MIN_YEARS_EXPERIENCE', 'SALARY']:
    features[col] = pd.to_numeric(features[col], errors='coerce')

features = features.dropna()

scaler = StandardScaler()
X = scaler.fit_transform(features)

kmeans = KMeans(n_clusters=4, random_state=688)
eda.loc[features.index, 'Cluster'] = kmeans.fit_predict(X)

true_labels = eda.loc[features.index, 'SOC_2021_4_NAME']
true_labels_encoded = LabelEncoder().fit_transform(true_labels)

ari = adjusted_rand_score(true_labels_encoded, eda.loc[features.index, 'Cluster'])
Code
import plotly.express as px
import plotly.graph_objects as go
from IPython.display import HTML

# 1) Build the DataFrame
df_plot = features.copy()
df_plot['Cluster'] = eda.loc[features.index, 'Cluster']

# 2) Compute centroids in original units
centroids = kmeans.cluster_centers_
centroids_x = centroids[:, 0] * X.std(axis=0)[0] + X.mean(axis=0)[0]
centroids_y = centroids[:, 1] * X.std(axis=0)[1] + X.mean(axis=0)[1]

# 3) Create an interactive Plotly Figure
fig = px.scatter(
    df_plot,
    x='SALARY',
    y='MAX_YEARS_EXPERIENCE',
    color='Cluster',
    title="KMeans Clustering by Salary and Max Years Experience",
    labels={
        'SALARY': 'Salary',
        'MAX_YEARS_EXPERIENCE': 'Max Years Experience',
        'Cluster': 'Cluster'
    },
    width=800,
    height=500,
)

# 4) Add centroid traces
fig.add_trace(
    go.Scatter(
        x=centroids_x,
        y=centroids_y,
        mode='markers',
        marker=dict(symbol='x', size=18, color='black', line=dict(width=2, color='white')),
        name='Centroids'
    )
)

# 5) Render the full HTML and embed it
html = fig.to_html(include_plotlyjs='cdn')
HTML(html)

Here we have 4 cluster groups. Group 0, which represent as green have lower salary, mostly under 150k, and max years experience in 2-5 years, it is likely Likely junior to mid-level employees with moderate pay. Group 1 with orange, has medium to high salary, wide range from $100k–$500k and with narrow range ~3 years, they are suggests specialized or high-paying roles with short experience — possibly fast-track promotions or high-demand fields. cluster 2 are low salary and experience from 0-4 years, they are clearly entry level employee. cluster 3 has medium salary, mostly under 200k with higher experiences, like 6-13 eyars. They probably are senior professionals with more experience but not the highest salaries.

Code
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score

features1 = eda[['MIN_YEARS_EXPERIENCE', 'MAX_YEARS_EXPERIENCE']].copy()

for col in features.columns:
    features[col] = pd.to_numeric(features[col], errors='coerce')

features = features.dropna()

X = features
y = eda.loc[X.index, 'SALARY']

X = X.dropna()
y = y.loc[X.index]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=688)

model = LinearRegression()
model.fit(X_train, y_train)

y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
Code
plt.figure(figsize =(10,6))
plt.scatter(y_test, y_pred, alpha = 0.6, color = 'skyblue')
plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], color='red', linestyle='--', lw=2)
plt.xlabel("Actual Salary")
plt.ylabel("Predicted Salary")
plt.title("Actual vs Predicted Salary (Multiple Regression)")
plt.grid(True)
plt.tight_layout()
plt.show()

This plot shows the Actual vs. Predicted Salary using a multiple linear regression model. The blue dots represent individual predictions, and the red dashed line is the ideal line where predicted = actual. Since most points lie very close to the red line, it means your model predicts salary very accurately, with minimal error and strong linear fit — likely reflected in a high R² score near 1.0.